Systems and methods for measuring radiated thermal energy during an additive manufacturing operation

ABSTRACT

This disclosure describes various methods and apparatus for characterizing an additive manufacturing process. A method for characterizing the additive manufacturing process can include generating scans of an energy source across a build plane; measuring an amount of energy radiated from the build plane during each of the scans using an optical sensor; determining an area of the build plane traversed during the scans; determining a thermal energy density for the area of the build plane traversed by the scans based upon the amount of energy radiated and the area of the build plane traversed by the scans; mapping the thermal energy density to one or more location of the build plane; determining that the thermal energy density is characterized by a density outside a range of density values; and thereafter, adjusting subsequent scans of the energy source across or proximate the one or more locations of the build plane.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is continuation of U.S. patent application Ser. No.16/052,488, now U.S. Pat. No. 10,479,020, filed Aug. 1, 2018; whichclaims priority to U.S. Provisional Patent Application Nos. 62/540,016,filed on Aug. 1, 2017, 62/633,487, filed on Feb. 21, 2018 and62/643,457, filed on Mar. 15, 2018. The disclosures of which are herebyincorporated by reference in their entirety and for all purposes.

BACKGROUND OF THE INVENTION

Additive manufacturing, or the sequential assembly or construction of apart through the combination of material addition and applied energy,takes on many forms and currently exists in many specificimplementations and embodiments. Additive manufacturing can be carriedout by using any of a number of various processes that involve theformation of a three dimensional part of virtually any shape. Thevarious processes have in common the sintering, curing or melting ofliquid, powdered or granular raw material, layer by layer usingultraviolet light, high powered laser, or electron beam, respectively.Unfortunately, established processes for determining a quality of aresulting part manufactured in this way are limited. Conventionalquality assurance testing generally involves post-process measurementsof mechanical, geometrical, or metallurgical properties of the part,which frequently results in destruction of the part. While destructivetesting is an accepted way of validating a part's quality, as it allowsfor close scrutiny of various internal features of the part, such testscannot for obvious reasons be applied to a production part.Consequently, ways of non-destructively and accurately verifying themechanical, geometrical and metallurgical properties of a productionpart produced by additive manufacturing are desired.

SUMMARY OF THE INVENTION

The described embodiments are related to additive manufacturing, whichinvolves using an energy source that takes the form of a moving regionof intense thermal energy. In the event that this thermal energy causesphysical melting of the added material, then these processes are knownbroadly as welding processes. In welding processes, the material, whichis incrementally and sequentially added, is melted by the energy sourcein a manner similar to a fusion weld. Exemplary welding processessuitable for use with the described embodiments include processes usinga scanning energy source with powder bed and wire-fed processes usingeither an arc, laser or electron beam as the energy source.

When the added material takes the form of layers of powder, after eachincremental layer of powder material is sequentially added to the partbeing constructed, the scanning energy source melts the incrementallyadded powder by welding regions of the powder layer creating a movingmolten region, hereinafter referred to as the melt pool, so that uponsolidification they become part of the previously sequentially added andmelted and solidified layers below the new layer to form the part beingconstructed. As additive machining processes can be lengthy and includeany number of passes of the melt pool, it can be difficult to avoid atleast slight variations in the size and temperature of the melt pool asthe melt pool is used to solidify the part. Embodiments described hereinreduce or minimize discontinuities caused by the variations in size andtemperature of the melt pool. It should be noted that additivemanufacturing processes can be driven by one or more processorsassociated with a computer numerical control (CNC) due to the high ratesof travel of the heating element and complex patterns needed to form athree dimensional structure.

An overall object of the described embodiments is to apply opticalsensing techniques for example, quality inference, process control, orboth, to additive manufacturing processes. Optical sensors can be usedto track the evolution of in-process physical phenomena by tracking theevolution of their associated in-process physical variables. Hereinoptical can include that portion of the electromagnetic spectrum thatincludes near infrared (IR), visible, and well as near ultraviolet (UV).Generally the optical spectrum is considered to go from 380 nm to 780 nmin terms of wavelength. However near UV and IR could extend as low as 1nm and as high as 3000 nm in terms of wavelength respectively. Sensorreadings collected from optical sensors can be used to determine inprocess quality metrics (IPQMs). One such IPQM is thermal energy density(TED), which is helpful in characterizing the amount of energy appliedto different regions of the part.

TED is a metric that is sensitive to user-defined laser powder bedfusion process parameters, for example, laser power, laser speed, hatchspacing, etc. This metric can then be used for analysis using IPQMcomparison to a baseline dataset. The resulting IPQM can be calculatedfor every scan and displayed in a graph or in three dimensions using apoint-cloud. Also, IPQM comparisons to the baseline dataset indicativeof manufacturing defects may be used to generate control signals forprocess parameters. In some embodiments, where detailed thermal analysisis desired, thermal energy density can be determined for discreteportions of each scan. In some embodiments, thermal energy data frommultiple scans can be divided into discrete grid regions of a grid,allowing each grid region to reflect a total amount of energy receivedat each grid region for a layer or a predefined number of layers.

An additive manufacturing method is disclosed and includes thefollowing: generating a plurality of scans of an energy source across abuild plane; measuring an amount of energy radiated from the build planeduring each of the plurality of scans using an optical sensor monitoringthe build plane; determining an area of the build plane traversed duringthe plurality of scans; determining a thermal energy density for thearea of the build plane traversed by the plurality of scans based uponthe amount of energy radiated and the area of the build plane traversedby the plurality of scans; mapping the thermal energy density to one ormore location of the build plane; determining that the thermal energydensity is characterized by a density outside a range of density values;and thereafter, adjusting subsequent scans of the energy source acrossor proximate the one or more locations of the build plane.

An additive manufacturing method is disclosed and includes thefollowing: generating a scan of an energy source across a build plane;measuring an amount of energy radiated from the powder bed during thescan using an optical sensor monitoring the powder bed; determining anarea associated with the scan; determining a thermal energy density forthe area of the scan based upon the amount of energy radiated and thearea of the scan; determining that the thermal energy density ischaracterized by a density outside a range of density values; andthereafter, adjusting a subsequent scan of the energy source across thebuild plane.

An additive manufacturing method is disclosed and includes the followingperforming an additive manufacturing operation using an energy source;receiving sensor data associated with a photodiode during a scan of theenergy source across a powder bed; receiving drive signal data thatindicates when the energy source is powered on; identifying sensor datacollected when the energy source is powered on using the energy sourcedrive signal data; dividing the sensor data into a plurality of samplesections, each of the sample sections corresponding to a portion of ascan, determining a thermal energy density for each of the plurality ofsample sections; and identifying one or more portions of the part mostlikely to contain manufacturing defects based on the thermal energydensity of each of the plurality of sample sections.

An additive manufacturing method is disclosed and includes thefollowing: generating a plurality of scans of an energy source across abuild plane; determining a grid region including the plurality of scans,wherein the grid region is characterized by a grid area; generatingsensor readings during each of the plurality of scans using an opticalsensor; determining a total amount of energy radiated from the buildplane during the plurality of scans using the sensor readings; computinga thermal energy density associated with the grid region based upon thetotal amount of energy radiated and the grid area; determining that thethermal energy density associated with the grid region is characterizedby a thermal energy density outside a range of thermal energy densityvalues; and thereafter, adjusting an output of the energy source.

An additive manufacturing method is disclosed and includes thefollowing: defining a portion of a build plane as a grid including aplurality of grid regions each having a grid region area; generating aplurality of scans of an energy source across the build plane;generating sensor readings during each of the plurality of scans usingan optical sensor; for each of the plurality of scans, mapping portionsof each of the plurality of sensor readings to a respective one of theplurality of grid regions; for each of the plurality of grid regions:summing the sensor readings mapped to each grid region; and computing agrid-based thermal energy density based on the summed sensor readingsand the grid region area; determining that the grid-based thermal energydensity associated with one or more of the plurality of grid regions ischaracterized by a thermal energy density outside a range of thermalenergy density values; and thereafter, adjusting an output of the energysource.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be readily understood by the following detaileddescription in conjunction with the accompanying drawings, wherein likereference numerals designate like structural elements, and in which:

FIG. 1A is a schematic illustration of an optical sensing apparatus usedin an additive manufacturing system with an energy source, in thisspecific instance taken to be a laser beam;

FIG. 1B is a schematic illustration of an optical sensing apparatus usedin an additive manufacturing system with an energy source, in thisspecific instance taken to be an electron beam;

FIG. 2 shows sample scan patterns used in additive manufacturingprocesses;

FIG. 3 shows a flow chart representing a method for identifying portionsof the part most likely to contain manufacturing defects;

FIGS. 4A-4H show the data associated with the step by step process toidentify a portion of the part most likely to contain a manufacturingdefect using the thermal energy density;

FIG. 5 shows a flow chart describing in detail how to use scanlet datasegregation to complete an IPQM assessment;

FIGS. 6A-6F show the data associated with the step by step process toidentify a portion of the part most likely to contain a manufacturingdefect using the thermal energy density; and

FIGS. 7A-7C show test results comparing IPQM metrics to post processmetallography;

FIG. 8 shows an alternative process in which data recorded by an opticalsensor such as a non-imaging photodetector can be processed tocharacterize an additive manufacturing build process;

FIGS. 9A-9D show visual depictions indicating how multiple scans cancontribute to the power introduced at individual grid regions;

FIG. 10A shows an exemplary turbine blade suitable for use with thedescribed embodiments;

FIG. 10B shows an exemplary manufacturing configuration in which 25turbine blades can be concurrently manufactured atop a build plane 1006;

FIGS. 10C-10D show different cross-sectional views of different layersof the configuration depicted in FIG. 10B;

FIGS. 11A-11B show cross-sectional views of base portions of twodifferent turbine blades;

FIG. 11C shows a picture illustrating the difference in surfaceconsistency between two different base portions;

FIG. 12 illustrates thermal energy density for parts associated withmultiple different builds;

FIGS. 13-14B illustrate an example of how thermal energy density can beused to control operation of a part using in-situ measurements;

FIG. 14C shows another power-density graph emphasizing various physicaleffects resulting from energy source settings falling too far out of aprocess window;

FIG. 14D shows how a size and shape of a melt pool can vary inaccordance with laser power and scanning velocity settings;

FIGS. 15A-15F illustrate how a grid can be dynamically created tocharacterize and control an additive manufacturing operation;

FIG. 16 shows an exemplary control loop 1600 for establishing andmaintaining feedback control of an additive manufacturing operation;

FIG. 17A shows a normal distribution of powder across a build plate;

FIG. 17B shows how when an insufficient amount of powder is retrievedand spread across the build plate by a recoater arm, a thickness of aresulting layer of powder can vary;

FIG. 17C shows a black and white photo of a build plate in which a shortfeed of powder resulted in only partial coverage of nine workpiecesarranged on a build plate; and

FIG. 17D shows how when an energy source scans across all nineworkpieces using the same input parameters, detected thermal energydensity is substantially different.

DETAILED DESCRIPTION

FIG. 1A shows an embodiment of an additive manufacturing system thatuses one or more optical sensing apparatus to determine the thermalenergy density. The thermal energy density is sensitive to changes inprocess parameters such as, for example, energy source power, energysource speed, and hatch spacing. The additive manufacturing system ofFIG. 1A uses a laser 100 as the energy source. The laser 100 emits alaser beam 101 which passes through a partially reflective mirror 102and enters a scanning and focusing system 103 which then projects thebeam to a small region 104 on the work platform 105. In someembodiments, the work platform is a powder bed. Optical energy 106 isemitted from the small region 104 on account of high materialtemperatures.

In some embodiments, the scanning and focusing system 103 can beconfigured to collect some of the optical energy 106 emitted from thebeam interaction region 104. The partially reflective mirror 102 canreflect the optical energy 106 as depicted by optical signal 107. Theoptical signal 107 may be interrogated by multiple on-axis opticalsensors 109 each receiving a portion of the optical signal 107 through aseries of additional partially reflective mirrors 108. It should benoted that in some embodiments, the additive manufacturing system couldonly include one on-axis optical sensor 109 with a fully reflectivemirror 108.

It should be noted that the collected optical signal 107 may not havethe same spectral content as the optical energy 106 emitted from thebeam interaction region 104 because the signal 107 has suffered someattenuation after going through multiple optical elements such aspartially reflective mirror 102, scanning and focusing system 103, andthe series of additional partially reflective mirrors 108. These opticalelements may each have their own transmission and absorptioncharacteristics resulting in varying amounts of attenuation that thuslimit certain portions of the spectrum of energy radiated from the beaminteraction region 104. The data generated by on-axis optical sensors109 may correspond to an amount of energy imparted on the work platform.

Examples of on-axis optical sensors 109 include but are not limited tophoto to electrical signal transducers (i.e. photodetectors) such aspyrometers and photodiodes. The optical sensors can also includespectrometers, and low or high speed cameras that operate in thevisible, ultraviolet, or the infrared frequency spectrum. The on-axisoptical sensors 109 are in a frame of reference which moves with thebeam, i.e., they see all regions that are touched by the laser beam andare able to collect optical signals 107 from all regions of the workplatform 105 touched as the laser beam 101 scans across work platform105. Because the optical energy 106 collected by the scanning andfocusing system 103 travels a path that is near parallel to the laserbeam, sensors 109 can be considered on-axis sensors.

In some embodiments, the additive manufacturing system can includeoff-axis sensors 110 that are in a stationary frame of reference withrespect to the laser beam 101. These off-axis sensors 110 will have agiven field of view 111 which could be very narrow or it could encompassthe entire work platform 105. Examples of these sensors could includebut are not limited to pyrometers, photodiodes, spectrometers, high orlow speed cameras operating in visible, ultraviolet, or IR spectralranges, etc. Off-axis sensors 110, not aligned with the energy source,are considered off-axis sensors. Off-axis sensors 110 could also besensors which combine a series of physical measurement modalities suchas a laser ultrasonic sensor which could actively excite or “ping” thedeposit with one laser beam and then use a laser interferometer tomeasure the resultant ultrasonic waves or “ringing” of the structure inorder to measure or predict mechanical properties or mechanicalintegrity of the deposit as it is being built. The laser ultrasonicsensor/interferometer system can be used to measure the elasticproperties of the material, which can provide insight into, for example,the porosity of the material and other materials properties.Additionally, defect formation that results in material vibration can bemeasured using the laser ultrasonic/sensor interferometer system.

Additionally, there could be contact sensors 113 on the mechanicaldevice, recoater arm 112, which spreads the powders. These sensors couldbe accelerometers, vibration sensors, etc. Lastly, there could be othertypes of sensors 114. These could include contact sensors such asthermocouples to measure macro thermal fields or could include acousticemission sensors which could detect cracking and other metallurgicalphenomena occurring in the deposit as it is being built. These contactsensors can be utilized during the powder addition process tocharacterize the operation of the recoater arm 112. Data collected bythe on-axis optical sensors 109 and the off-axis sensors 110 can be usedto detect process parameters associated with the recoater arm 112.Accordingly, non-uniformities in the surface of the spread powder can bedetected and addressed by the system. Rough surfaces resulting fromvariations in the powder spreading process can be characterized bycontact sensors 113 in order to anticipate possible problem areas ornon-uniformities in the resulting part.

In some embodiments, a peak in the powder spread can be fused by thelaser beam 101, resulting in the subsequent layer of powder having acorresponding peak. At some point, the peak could contact the recoaterarm 112, potentially damaging the recoater arm 112 and resulting inadditional spread powder non-uniformity. Accordingly, embodiments of thepresent invention can detect the non-uniformities in the spread powderbefore they result in non-uniformities in the build area on the workplatform 105. One of ordinary skill would recognize many variations,modifications, and alternatives.

In some embodiments, the on-axis optical sensors 109, off-axis sensors110, contact sensors 113, and other sensors 114 can be configured togenerate in-process raw sensor data. In other embodiments, the on-axisoptical sensors 109, off-axis optical sensors 110, contact sensors 113,and other sensors 114 can be configured to process the data and generatereduced order sensor data.

In some embodiments, a computer 116, including a processor 118, computerreadable medium 120, and an I/O interface 122, is provided and coupledto suitable system components of the additive manufacturing system inorder to collect data from the various sensors. Data received by thecomputer 116 can include in-process raw sensor data and/or reduced ordersensor data. The processor 118 can use in-process raw sensor data and/orreduced order sensor data to determine laser 100 power and controlinformation, including coordinates in relation to the work platform 105.In other embodiments, the computer 116, including the processor 118,computer readable medium 120, and an I/O interface 122, can provide forcontrol of the various system components. The computer 116 can send,receive, and monitor control information associated with the laser 100,the work platform 105, and the recoater arm 112 in order to control andadjust the respective process parameters for each component.

The processor 118 can be used to perform calculations using the datacollected by the various sensors to generate in process quality metrics.In some embodiments, data generated by on-axis optical sensors 109,and/or the off-axis sensors 110 can be used to determine the thermalenergy density during the build process. Control information associatedwith movement of the energy source across the build plane can bereceived by the processor. The processor can then use the controlinformation to correlate data from on-axis optical sensor(s) 109 and/oroff-axis optical sensor(s) 110 with a corresponding location. Thiscorrelated data can then be combined to calculate thermal energydensity. In some embodiments, the thermal energy density and/or othermetrics can be used by the processor 118 to generate control signals forprocess parameters, for example, laser power, laser speed, hatchspacing, and other process parameters in response to the thermal energydensity or other metrics falling outside of desired ranges. In this way,a problem that might otherwise ruin a production part can beameliorated. In embodiments where multiple parts are being generated atonce, prompt corrections to the process parameters in response tometrics falling outside desired ranges can prevent adjacent parts fromreceiving too much or too little energy from the energy source.

In some embodiments, the I/O interface 122 can be configured to transmitdata collected to a remote location. The I/O interface can be configuredto receive data from a remote location. The data received can includebaseline datasets, historical data, post-process inspection data, andclassifier data. The remote computing system can calculate in-processquality metrics using the data transmitted by the additive manufacturingsystem. The remote computing system can transmit information to the I/Ointerface 122 in response to particular in-process quality metrics.

In the case of an electron beam system, FIG. 1B shows possibleconfigurations and arrangements of sensors. The electron beam gun 150generates an electron beam 151 that is focused by the electromagneticfocusing system 152 and is then deflected by the electromagneticdeflection system 153 resulting in a finely focused and targetedelectron beam 154. The electron beam 154 creates a hot beam-materialinteraction zone 155 on the workpiece 156. Optical energy 158 isradiated from workpiece 156 which could be collected by a series ofoptical sensors 159, each with their own respective field of view 160which, again, could be locally isolated to the interaction region 155 orcould encompass the entire workpiece 156. Additionally, optical sensors159 could have their own tracking and scanning system which could followthe electron beam 154 as it moves across the workpiece 156.

Whether or not sensors 159 have optical tracking, the sensors 159 couldconsist of pyrometers, photodiodes, spectrometers, and high or low speedcameras operating in the visible, UV, or IR spectral regions. Thesensors 159 could also be sensors which combine a series of physicalmeasurement modalities such as a laser ultrasonic sensor which couldactively excite or “ping” the deposit with one laser beam and then use alaser interferometer to measure the resultant ultrasonic waves or“ringing” of the structure in order to measure or predict mechanicalproperties or mechanical integrity of the deposit as it is being built.Additionally, there could be contact sensors 113 on the recoater arm.These sensors could be accelerometers, vibration sensors, etc. Lastly,there could be other types of sensors 114. These could include contactsensors such as thermocouples to measure macro thermal fields or couldinclude acoustic emission sensors which could detect cracking and othermetallurgical phenomena occurring in the deposit as it is being built.In some embodiments, one or more thermocouples could be used tocalibrate temperature data gathered by sensors 159. It should be notedthat the sensors described in conjunction with FIGS. 1A and 1B can beused in the described ways to characterize performance of any additivemanufacturing process involving sequential material buildup.

FIG. 2 illustrates possible hatch patterns for scanning an energy sourceacross a powder bed. In 200, a region of the workpiece is processed bythe energy source scanning along long path lengths that alternate indirection. In this embodiment, hatch spacing 204 is shown between afirst scan 206 and a second scan 208. In 202, a region of the workpieceis broken into smaller checkerboards 214 which can be scanned by a firstscan 210 and a second scan 212 sequentially left to right and top tobottom. In other embodiments, the scan order for the individualcheckerboards can be randomized. A number of hatch patterns can beutilized in conjunction with the additive manufacturing processdisclosed herein. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

FIG. 3 shows a flowchart that illustrates an exemplary process 300 thatuses data generated by an additive manufacturing system to determine athermal energy density and identify portions of a part most likely tocontain manufacturing defects. Data generated by the on-axis opticalsensors 109 and the off-axis optical sensors 110 can be used alone or incombination to determine the thermal energy density. At 302, a rawphotodiode data trace is received. The raw photodiode data trace can begenerated using, for example, voltage data generated by the sensor inresponse to detection of emitted heat energy. At 304, a portion of theraw photodiode trace that corresponds to a particular scan, scan_(i) isidentified. In some embodiments, the individual photodiode data tracecan be separated from the rest of the sensor readings by referencingenergy source drive signal data (drive signal responsible formaneuvering and actuating the energy source). At 306, determine the areaunder the raw photodiode data trace for scam, hereinafter, pdon_(i). Insome embodiments, pdon_(i) can represent the integrated photodiodevoltage. In some embodiments, pdon_(i) represents the average reading ofthe photodiode during scam. At 308, identify the part, p, associatedwith scam. The part identified at 308 can also have an associated areaof the part, A_(p). These two values can be determined by correlatingpdon_(i) with energy source location data as described above. Theprocess can, at 310, calculate the total scan count. At 312 a lengthassociated with scam, L_(i) can be determined. L_(i) can be calculatedusing equation (1), where x1_(i), y1_(i), and x2_(i), y2_(i) representrespective beginning and end locations for scan_(i):L ₁=√{square root over ((x1_(i) −x2_(i))²+(y1_(i) −y2_(i))²)}  Eq(1)

At 314, the total length of all scans used to produce the part,Lsum_(p), can be determined. The Lsum_(p) over the part can bedetermined by summing the length of each scan, L_(i), associated withthe part. At 316, the prorated area of the scan, A_(i), can bedetermined. A_(i) can be calculated using equation (2):

$\begin{matrix}{A_{i} = \frac{\left( {A_{p}*L_{i}} \right)}{{Lsum}_{p}}} & {{Eq}\mspace{14mu}(2)}\end{matrix}$

At 316, the prorated thermal energy density (TED) for the i^(th) scan,TED_(i), can be determined. TED_(i) is an example of a set of reducedorder process features. The TED is calculated using raw photodiode data.From this raw sensor data, the TED calculation extracts reduced orderprocess features from the raw sensor data. TED_(i) is sensitive to alluser defined laser powder bed fusion process parameters, for examplelaser power, laser speed, hatch spacing, and many more. TED_(i) can becalculated using equation (3):

$\begin{matrix}{{TED}_{i} = \frac{{pdon}_{i}}{A_{i}}} & {{Eq}\mspace{14mu}(3)}\end{matrix}$

For the purposes of this discussion “reduced order” refers to one ormore of the following aspects: data compression, i.e., less data in thefeatures as compared to the raw data; data reduction, i.e. a systematicanalysis of the raw data which yields process metrics or other figuresof merit; data aggregation, i.e. the clustering of data into discretegroupings and a smaller set of variables that characterize theclustering as opposed to the raw data itself; data transformation, i.e.the mathematical manipulation of data to linearly or non-linearly mapthe raw data into another variable space of lower dimensionality using atransformation law or algorithm; or any other related such techniqueswhich will have the net effect of reducing data density, reducing datadimensionality, reducing data size, transforming data into anotherreduced space, or all of these either effected simultaneously.

TED_(i) can be used for analysis during in process quality metric (IPQM)comparison to a baseline dataset. A resulting IPQM can be calculated forevery scan. At 318, the IPQM quality baseline data set and thecalculated TED_(i) can be compared. In regions of the part where adifference between the calculated TED and baseline data set exceeds athreshold value, those regions can be identified as possibly includingone or more defects and/or further processing can be performed on theregion in near real-time to ameliorate any defects caused by thevariation of TED from the baseline data set. In some embodiments, theportions of the part that may contain defects can be identified using aclassifier. The classifier is capable of grouping the results as beingeither nominal or off-nominal and could be represented through graphicaland/or text-based mediums. The classifier could use multipleclassification methods including, but not limited to: statisticalclassification, both single and multivariable; heuristic basedclassifiers; expert system based classifiers; lookup table basedclassifiers; classifiers based simply on upper or lower control limits;classifiers which work in conjunction with one or more statisticaldistributions which could establish nominal versus off-nominalthresholds based on confidence intervals and/or a consideration of thedegrees of freedom; or any other classification scheme whether implicitor explicit which is capable of discerning whether a set of feature datais nominal or off-nominal. For the purposes of this discussion,“nominal” will mean a set of process outcomes which were within apre-defined specification, which result in post-process measuredattributes of the parts thus manufactured falling within a regime ofvalues which are deemed acceptable, or any other quantitative,semi-quantitative, objective, or subjective methodology for establishingan “acceptable” component. Additional description related toclassification of IPQMs is provided in U.S. patent application Ser. No.15/282,822, filed on Sep. 30, 2016, the disclosure of which is herebyincorporated by reference in its entirety for all purposes.

It should be appreciated that the specific steps illustrated in FIG. 3provide a particular method of collecting data and determining thethermal energy density according to an embodiment of the presentinvention. Other sequences of steps may also be performed according toalternative embodiments. Moreover, the individual steps illustrated inFIG. 3 may include multiple substeps that may be performed in varioussequences as appropriate to the individual step. Furthermore, additionalsteps may be added or existing steps may be removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIGS. 4A-4H illustrate the steps used in the process 300 to determinethe TED and identify any portions of the part likely to contain defects.FIG. 4A corresponds to step 302 and shows a raw photodiode signal 402for a given scan length. The x-axis 450 indicates time in seconds andthe y-axis 460 indicates the photodiode voltage. In some embodiments,optical measurements could instead, or in addition to, be made by apyrometer. The signal 402 is the photodiode raw voltage. The rise 404and fall 406 of the photodiode signal 402 can be clearly seen as well asthe scatter and variation 408 in the signal during the time that thelaser is on. The data is collected at a given number of samples persecond. The variation 408 in photodiode signal 402 can be caused byvariations in powder being melted on the powder bed. For example, one ofthe minor troughs of photodiode signal 402 can be caused by energy beingabsorbed by a larger particle in the particle bed transitioning from asolid state to a liquid state. In general, the number of data points ina given segment of the photodiode signal between rise and fall eventscan be related to the scan duration and the sampling rate.

FIG. 4B shows a raw photodiode signal 402 and a laser drive signal 410.The laser drive signal 410 depicted in FIG. 4B can be produced usingenergy source drive signal data, in this case the laser drive signal410, or a command signal which tells the laser to turn on and off for aspecific scan length. The photodiode signal 402 is superimposed over thelaser drive signal 410. The rise 412 and fall 414 of the laser drivesignal 410 correspond to the rise 404 and fall 406 of the photodiodesignal 402. The data illustrated in FIG. 4B can be used to at step 304to identify a portion of the raw photodiode signal 402 that correspondsto a scan. In some embodiments, the laser drive signal 410 is ˜0V whenthe laser is off and ˜5 V when the laser is on. Step 304 can beaccomplished by isolating all the data associated with the photodiodesignal where the laser drive signal 410 is above a certain threshold,for example, 4.5V, and exclude all data where the laser is below thisthreshold from analysis.

FIG. 4C shows one embodiment of step 306, which includes determining thearea 416 under the raw photodiode signal 402. In some embodiments, thearea under the curve can be calculated using equation (4):pdon_(i)=∫_(rise) ^(fall) V(t)dt  Eq(4)The integrated photodiode voltage 418 can be used to determine pdon_(i)for the TED_(i) calculation.

FIG. 4D shows a position of scan 420 relative to the part and the totalscan count 424. Both values can be used to determine a TED thatcorresponds to the scan location on the part. FIG. 4E shows the renderarea for a part of interest 426. FIG. 4E also shows a plurality ofadditional parts 428 and a witness coupon 430. All of the parts in FIG.4E are depicted positioned on a powder bed 432.

FIG. 4F shows a trace associated with a portion of the photodiode dataand the laser drive signal data corresponding to four scans that can beused with the rest of the photodiode data to determine the total samplecount 434. The total sample count can be used to calculate the totalscan length over the part, LSum_(p). The total sample count isdetermined by summing the laser-on time periods 436. In someembodiments, the total scan length can be determined using the sum ofthe laser on time periods and average speed of the scanning energysource during laser-on time periods.

After collecting the scan data, the TED for each layer can be calculatedfrom the TED associated with each laser scan and then displayed in agraph 440, shown in FIG. 4G. The graph 440 illustrates TED valuespositioned within nominal region 442 and off nominal region 444. The TEDregions are divided by baseline threshold 438. In this way, layers ofthe part likely to contain defects are easily identifiable. Furtheranalysis could then be focused on the layers with off-nominal TEDvalues.

FIG. 4H shows how the TED value for each scan can be displayed in threedimensions using a point-cloud 446. Point cloud 446 illustrates theposition in three-dimensional space of TED values from nominal region442 and off nominal region 444 by displaying off nominal values as adifferent color or intensity than nominal values. Off nominal values areindicative of portions of the part most likely to contain manufacturingdefects, such as, porosity from keyhole formation or voids resultingfrom a lack of fusion. In some embodiments, the system can generate andtransmit a control signal that will change one or more processparameters based on the TED.

FIG. 5 shows a flowchart that illustrates an exemplary process 500 thatuses data generated by an additive manufacturing system to determine athermal energy density and identify portions of a part most likely tocontain manufacturing defects. Data generated by the on-axis opticalsensors 109 and the off-axis optical sensors 110 can be used alone or incombination to determine the thermal energy density. At 502, photodiodetime series data can be collected. The photodiode time series data canbe generated using, for example, voltage data associated with thesensors. At 504, laser drive time series data is collected. The laserdrive time data may be associated with additional process parameterssuch as, laser power, laser speed, hatch spacing, x-y position, etc. Theprocess at 506 can slice the photodiode time series data by droppingportions of the photodiode time series data that correspond to portionsof the laser drive time series data that indicate a laser-off state. Insome embodiments, the laser drive signal is ˜0 V when the laser is offand ˜5 V when the laser is on. The process at 506 can isolate all thedata where the laser drive signal is above a certain threshold, forexample, 4.5 V, and exclude all data where the laser is below thisthreshold from analysis. In some embodiments, the photodiode signal thatdrops to ˜0.2 V periodically can be included in the sample series dataas these are times when the laser just turned on and the laser isheating the material.

The process at 506 outputs only the laser-on photodiode data 508. Thelaser on photodiode data can be used by the process at 510 to convertsthe time-series data into sample-series data. The process at 510segments the laser on photodiode data into ‘N’ sample sections. The useof 20 sample sections is meant to provide an example of one embodimentof the present invention. Any number of sample sections can be used withvarying degrees of accuracy/resolution. In some embodiments, the set ofsample sections can be referred to as a scanlet 520 since it generallytakes multiple scanlets 520 to make up a single scan. The process at 512can count the number of samples 516. The process at 514 can render anarea of the lased part. In some embodiments, the lased part area 518 canbe determined using the number of pixels in a display associated withthe lased part. In other embodiments, the area can be calculated usingthe number of scans and data associated with process parameters. At 522,a process normalizes the scanlet data using total sample count lasedpart area 518, and scanlet data 520. In the illustrated embodiment, thescanlet metric data 524 is the thermal energy density for portions ofthe part associated with each scanlet. In some embodiments, scan datacan also be broken down by scan type. For example, an additivemanufacturing machine can utilize scans having differentcharacteristics. In particular, contour scans, or those designed tofinish an outer surface of a part can have substantially more power thanscans designed to sinter interior regions of a part. For this reason,more consistent results can be obtained by also segregating the data byscan type. In some embodiments, identification of scan types can bebased on scan intensity, scan duration and/or scan location. In someembodiments, scan types can be identified by correlating the detectedscans with scans dictated by a scan plan associated with the part beingbuilt.

Next, a process 528 receives baseline scanlet metric data and thethermal energy density and outputs an IPQM quality assessment 530. TheIPQM quality assessment 530 can be used to identify portions of the partmost likely to contain manufacturing defects. The process 528 caninclude a classifier as discussed earlier in the specification. Inaddition to the methods and systems above, the process 528 can comparethe candidate data, for example the scanlet metric data 524 and thebaseline scanlet metric data using a Mahalanobis distance. In someembodiments, the Mahalanobis distance for each scanlet can be can becalculated using the baseline scanlet metric data. While the embodimentsdisclosed in relation to FIG. 5 discussed the use of a laser as anenergy source, it will be apparent to one of ordinary skill in the artthat many modifications and variations are possible in view of the aboveteachings, for example, the laser may be replaced with an electron beamor other suitable energy source.

It should be appreciated that the specific steps illustrated in FIG. 5provide a particular method of determining a thermal energy density andidentifying portions of a part most likely to contain manufacturingdefects according to another embodiment of the present invention. Othersequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 5 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 6A shows photodiode time series data 602. The photo diode timeseries data can be collected from a variety of on-axis or off-axissensors as illustrated in FIGS. 1 and 2. The x-axis 604 indicates timein seconds and the y-axis 606 indicates voltage generated by the sensor.The voltage generated by the sensor is associated with energy emittedfrom the build plane that can impinge on one or more sensors. Thesamples 606 are illustrated on the trace of the photodiode time seriesdata 602. FIG. 4B describes the process at 506 where the photodiode datais associated with the laser drive signal.

FIG. 6B shows the laser on photodiode data. The x-axis represents thenumber of samples 608 and the y-axis 610 represents the voltage of theraw sensor data. The drops 612 in voltage are included in the analysisbecause, while the voltage is substantially lower, the laser is stillactively contributing to heating the material.

FIG. 6C shows the laser on photodiode sample series data discussed inrelation to step 510. The 20-sample sections 620 can have an arbitrarysize. Twenty samples corresponds to a laser travel distance of ˜400 μmwith a laser travel speed of 1000 mm/s. The noise in the XY signalitself is ˜150 μm. In some embodiments, with less than a 20-samplesection, for example, a 2 sample-section, the distance measured and thenoise would be in such a ratio that the location of a point could notconfidently be determined. In some embodiments, a limit of 50 samplescan be used for spatial resolution below 1 mm. Thus, the number ofsamples to segment the data into should be in the range of 20≤N≤50, for50 KHz data with a laser travel speed of 1000 mm/s. It will be apparentto one of ordinary skill in the art that many modifications andvariations are possible in view of the above teachings.

FIG. 6D corresponds to process 522 and illustrates an embodiment wherethe average value 618 of each scanlet is determined. In someembodiments, the inputs into process 522 include the total sample count516, the lased part area 518, and the scanlet data 520. Using theseinputs, the average can be used to determine the area under the curve(AUC) as illustrated in equation (5):AUC=V(avg)*N(samples)  Eq(5)Where V is the average voltage determined for each scanlet and N is thenumber of samples. In FIG. 6D, the average voltage of the 20-samplesegment is equivalent to integrating the signal because the width of thedata is fixed.

FIG. 6E shows the lased part area for an individual scanlet 622, A_(i),and for all scans 624. In addition to the area, the length of a scan,L_(i), and the sum of L_(i) over the entire part can be calculated,LSum_(p). L_(i) can be calculated using equation (6):L ₁=√{square root over ((x1_(i) −x2_(i))²+(y1_(i) −y2_(i))²)}  Eq(6)The x and y coordinates for the beginning and end of the scan may beprovided or they may be determined based on one or more direct sensormeasurements.

FIG. 6F shows the render area 626 of a lased part associated with alayer in the build plane. In some embodiments, once pdon_(i), the areaof the part, A_(p), the length of the scan, L_(i), and the total lengthLSum_(p) are determined, TED can be calculated using equation (7):

$\begin{matrix}{{TED}_{i} = \frac{\left( {{pdon}_{i}*{LSum}_{p}} \right)}{\left( {A_{p}*L_{i}} \right)}} & {{Eq}\mspace{14mu}(7)}\end{matrix}$TED is sensitive to all user-defined laser powder bed fusion processparameters, for example, laser power, laser speed, hatch spacing, etc.The TED value can be used for analysis using an IPQM comparison to abaseline dataset. The resulting IPQM can be determined for every laserscan and displayed in a graph or in three dimensions using apoint-cloud. FIG. 4G shows an exemplary graph. FIG. 4H shows anexemplary point cloud.

FIGS. 7A-7C show post process porosity measurements and correspondingnormalized in-process TED measurements. The figures show that in-processTED measurements can be an accurate IPQM predictor of porosity and othermanufacturing defects. FIG. 7A shows the comparison of TED metric datato a baseline dataset. The plot shows the value of each photodiode inthe IPQM metric, both separate and combined. On-axis photodiode data 702can come from sensors aligned with the energy source. Off-axisphotodiode data 704 can be collected by sensors that are not alignedwith the energy source. The combination of on-axis and off-axisphotodiode data 706 yields the highest sensitivity to changes in processparameters. The x-axis 708 shows the build plane layer of the part; they-axis 710 shows the Mahalanobis distance between the calculated TED andthe baseline metric.

The Mahalanobis distance can be used to standardize the TED data. TheMahalanobis distance indicates how many standard deviations each TEDmeasurement is from a nominal distribution of TED measurements. In thiscase, the Mahalanobis Distance indicates how many standard deviationsaway each TED measurement is from the mean TED measurement collectedwhile building control layers 526-600. The chart below FIG. 7A alsoshows how TED varies with global energy density (GED) and porosity. Inparticular, for this set of experiments TED can be configured to predictpart porosity without the need to do destructive examination.

In some embodiments, the performance of the additive manufacturingdevice can be further verified by comparing quantitative metallographicfeatures (e.g. the size and shape of pores or intermetallic particles)and/or mechanical property features (e.g. strength, toughness orfatigue) of the metal parts created while performing the test runs. Ingeneral, the presence of unfused metal powder particles in the testparts indicates not enough energy was applied while test parts thatreceived too much energy tend to develop internal cavities that can bothcompromise the integrity of the created part. Porosity 714 can berepresentative of these defects.

In some embodiments, a nominal value used to generate FIG. 7A will betaken from a preceding test. In some embodiments, the nominal valuecould also be taken from a subsequent test since the calculations do notneed to be done during the additive manufacturing operation. Forexample, when attempting to compare performance of two additivemanufacturing devices, a nominal value can be identified by running atest using a first one of the additive manufacturing devices. Theperformance of the second additive manufacturing device could then becompared to the nominal values defined by the first additivemanufacturing device. In some embodiments, where performance of the twoadditive manufacturing devices is within a predetermined threshold offive standard deviations, comparable performance can be expected fromthe two machines. In some embodiments, the predetermined threshold canbe a 95% statistical confidence level derived from an inverse chisquared distribution. This type of test methodology can also be utilizedin identifying performance changes over time. For example, aftercalibrating a machine, results of a test pattern can be recorded. Aftera certain number of manufacturing operations are performed by thedevice, the additive manufacturing device can be performed again. Theinitial test pattern performed right after calibration can be used as abaseline to identify any changes in the performance of the additivemanufacturing device over time. In some embodiments, settings of theadditive manufacturing device can be adjusted to bring the additivemanufacturing device back to its post-calibration performance.

FIG. 7B shows the post process metallography for a part constructedusing an additive manufacturing process. FIG. 7B shows the part 718 anda corresponding cross-section 720 of the part. The sections 1 through 11correspond to sections 1 through 11 in FIG. 7A. The changes in processparameters, and the resulting changes in porosity, can be seen in thecross-section view 720 of the part. In particular, sections 2 and 3 havethe highest porosity, 3.38% and 1.62% respectively. The higher porosityis shown in the cross-section by the increased number of defect marks722 in the sample part.

FIG. 7C shows the IPQM results with the corresponding cross-sectiondetermined during metallography. Each cross-section includes the energydensity 724 in J/mm² and the porosity 714. The samples with the highestnumber of defect marks 722 correspond to the TED measurements with thehighest standardized distances from the baseline. The plot illustratesthat low standardized distance can be predictive of higher density andlow porosity metallography while a high standardized Malahabonisdistance is highly correlated with high-porosity and poor metallography.For example, low power settings used in generating layers around layer200 result in a high porosity 714 and a large number of defect marks722. In comparison, using middle of the road settings on or around the600^(th) layer results in no identifiable defect marks 722 and thelowest recorded porosity value of 0.06%.

FIG. 8 shows an alternative process in which data recorded by an opticalsensor such as a non-imaging photodetector can be processed tocharacterize an additive manufacturing build process. At 802, raw sensordata is received that can include both build plane intensity data andenergy source drive signals correlated together. At 804, by comparingthe drive signal and build plane intensity data, individual scans can beidentified and located within the build plane. Generally the energysource drive signal will provide at least start and end positions fromwhich the area across which the scan extends can be determined. At 806,raw sensor data associated with an intensity or power of each scan canbe binned into corresponding X & Y grid regions. In some embodiments,the raw intensity or power data can be converted into energy units bycorrelating the dwell time of each scan in a particular grid region. Insome embodiments, each grid region can represent one pixel of an opticalsensor monitoring the build plane. It should be noted that differentcoordinate systems, such as polar coordinates, could be used to storegrid coordinates and that storage of coordinates should not be limitedto Cartesian coordinates. In some embodiments, different scan types canbe binned separately so that analysis can be performed solely onparticular scan types. For example, an operator may want to focus oncontour scans if those types of scans are most likely to includeunwanted variations. At 808, energy input at each grid region can besummed up so that a total amount of energy received at each grid regioncan be determined using equation (8).E_(pd) _(m) =Σ_(n=1) ^(pixel samples in grid cell) E_(pd) _(n)   Eq(8)

This summation can be performed just prior to adding a new layer ofpowder to the build plane or alternatively, summation may be delayeduntil a predetermined number of layers of powder have been deposited.For example, summation could be performed only after having depositedand fused portions of five or ten different layers of powder during anadditive manufacturing process. In some embodiments, a sintered layer ofpowder can add about 40 microns to the thickness of a part; however thisthickness will vary depending on a type of powder being used and athickness of the powder layer.

At 810, the standard deviation for the samples detected and associatedwith each grid region is determined. This can help to identify gridregions where the power readings vary by a smaller or greater amount.Variations in standard deviation can be indicative of problems withsensor performance and/or instances where one or more scans are missingor having power level far outside of normal operating parameters.Standard deviation can be determined using Equation (9).

$\begin{matrix}{E_{{pd}_{sm}} = \sqrt{\frac{1}{{\#\mspace{14mu}{sample}\text{-}{in}\text{-}{location}} - 1}{\sum\limits_{n = 1}^{{sample}\text{-}i\; n\text{-}{pixel}}\left( {E_{n} - \overset{\_}{E}} \right)^{2}}}} & {{Eq}\mspace{14mu}(9)}\end{matrix}$

At 812, a total energy density received at each grid region can bedetermined by dividing the power readings by the overall area of thegrid region. In some embodiments, a grid region can have a squaregeometry with a length of about 250 microns. The energy density for eachgrid region can be determined using Equation (10).

$\begin{matrix}{E_{{grid}\mspace{14mu}{location}} = \frac{\sum\limits_{n = 1}^{{sample}\text{-}i\; n\text{-}{pixel}}E_{{pd}_{n}}}{A_{{grid}\mspace{14mu}{location}}}} & {{Eq}\mspace{14mu}(10)}\end{matrix}$

At 814, when more than one part is being built, different grid regionscan be associated with different parts. In some embodiments, a systemcan included stored part boundaries that can be used to quicklyassociate each grid region and its associated energy density with itsrespective part using the coordinates of the grid region and boundariesassociated with each part.

At 816, an area of each layer of a part can be determined. Where a layerincludes voids or helps define internal cavities, substantial portionsof the layer may not receive any energy. For this reason, the affectedarea can be calculated by summing only grid regions identified asreceiving some amount of energy from the energy source. At 818, thetotal amount of power received by the grid regions within the portion ofthe layer associated with the part can be summed up and divided by theaffected area to determine energy density for that layer of the part.Area and energy density can be calculated using Equations (11) and (12).

$\begin{matrix}{A_{part} = {\sum\limits_{n = 1}^{{part}\mspace{14mu}{pixel}}{1\left( {E_{{pd}_{n}} > 0} \right)}}} & {{Eq}\mspace{14mu}(11)} \\{{IPQM}_{{part}_{layer}} = \frac{\sum\limits_{n = 1}^{{part}\mspace{14mu}{grid}\mspace{14mu}{locations}}E_{{pd}_{n}}}{A_{part}}} & {{Eq}\mspace{14mu}(12)}\end{matrix}$

At 820, the energy density of each layer can be summed together toobtain a metric indicative of the overall amount of energy received bythe part. The overall energy density of the part can then be comparedwith the energy density of other similar parts on the build plane. At822, the total energy from each part is summed up. This allows highlevel comparisons to be made between different builds. Build comparisonscan be helpful in identifying systematic differences such as variationsin powder and changes in overall power output. Finally at 824, thesummed energy values can be compared with other layers, parts or buildplanes to determine a quality of the other layers, parts or buildplanes.

It should be appreciated that the specific steps illustrated in FIG. 8provide a particular method of characterizing an additive manufacturingbuild process according to another embodiment of the present invention.Other sequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 8 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIGS. 9A-9D show visual depictions indicating how multiple scans cancontribute to the power introduced at individual grid regions. FIG. 9Adepicts a grid pattern made up of multiple grid regions 902 distributedacross a portion of a part being built by an additive manufacturingsystem. FIG. 9A also depicts a first pattern of energy scans 904extending diagonally across a grid regions 902. The first pattern ofenergy scans 902 can be applied by a laser or other intense source ofthermal energy scanning across grid 904. FIG. 9B shows how the energyintroduced across the part is represented in each of grid regions 902 bya singular gray scale color representative of an amount of energyreceived where darker shades of gray correspond to greater amounts ofenergy. It should be noted that in some embodiments the size of gridregions 902 can be reduced to obtain higher resolution data.Alternatively, the size of grid regions 902 could be increased to reducememory and processing power usage.

FIG. 9C shows a second pattern of energy scans 906 overlapping with atleast a portion of the energy scans of the first pattern of energyscans. As discussed in the text accompanying FIG. 8, where the first andsecond patterns of energy scans overlap, grid regions are shown in adarker shade to illustrate how energy from both scans has increased theamount of energy received over the overlapping scan patterns. Clearly,the method is not limited to two overlapping scans and could includemany other additional scans that would get added together to fullyrepresent energy received at each grid region.

FIG. 10A shows an exemplary turbine blade 1000 suitable for use with thedescribed embodiments. Turbine blade 1000 includes multiple differentsurfaces and includes a number of different features that require manydifferent types of complex scans to produce. In particular, turbineblade 1000 includes a hollow blade portion 1002 and a tapered baseportion 1004. FIG. 10B shows an exemplary manufacturing configuration inwhich 25 turbine blades 1000 can be concurrently manufactured atop buildplane 1006.

FIGS. 10C-10D show different cross-sectional views of different layersof the configuration depicted in FIG. 10B with grid TED basedvisualization layers. FIG. 10C shows layer 14 of turbine blades 1000 andthe TED based visualization layer illustrates how a lower end of selectones of base portion 1004 can define multiple voids in turbine blades1000-1, 1000-2 and 1000-3. Because energy density data is associatedwith discrete grid regions, these voids, which would otherwise becompletely concealed within the turbine blades are clearly visible inthis grid TED based visualization. FIG. 10D shows how an upper end ofbase portion 1004 can also define multiple concealed voids withinturbine blades 1000-1, 1000-2 and 1000-3, which are clearly discernablefrom the grid TED based visualization layer depicted in FIG. 10D.

FIGS. 11A-11B show cross-sectional views of base portions 1004-1 and1004-2 of two different turbine blades 1000. FIG. 11A shows base portion1004-1, which was produced using nominal manufacturing settings. Outersurfaces 1102 and 1104 of base portions 1004-1 receive substantiallymore energy than interior 1106 of base portion 1004-1. The increasedenergy input into the outer surfaces provides a more uniform hardenedsurface, resulting in an annealing effect being achieved along outersurfaces 1102 and 1104. This additional energy can be introduced usinghigher energy contour scans that target outer surfaces 1102 and 1104.FIG. 11B shows base portion 1004-2, which was produced with the samemanufacturing settings as 1004-2 with the exception of omitting thecontour scans. While all the scans utilized in the manufacturingoperation producing base portion 1004-2 were also included in producingbase portion 1004-1, summing energy density inputs for all the scanscovering each grid region allows an operator to clearly see a differencebetween base portions 1004-1 and 1004-2.

FIG. 11C shows the difference in surface consistency between baseportions 1004-1 and 1004-2. Clearly, omitting the contour scans for baseportion 1004-2 has a substantial effect on overall outer surfacequality. Outer surfaces for base portion 1004-1 are smoother and lessporous in consistency. The annealing effect on base portion 1004-1should also give it substantially more strength than base portion1004-2.

FIG. 12 illustrates thermal energy density for parts associated withmultiple different builds. Builds A-G each include thermal emission datafor about 50 different parts (represented by discrete circles 1202)built during the same additive manufacturing operation. Thisillustration shows how thermal emission data can be used to trackvariations between different builds. For example, lots A, B and C allhave similar TED distributions; however, builds D, E and F while stillbeing within tolerances have consistently lower thermal emission data.In some cases, these types of changes can be due to changes in powderlots. In this way, the thermal emission data can be used to tracksystematic errors that may negatively affect overall output quality. Itshould be noted that while this chart depicts mean TED values based ongrid TED that a similar chart could be constructed for a scan-based TEDmethodology.

FIGS. 13-14B illustrate an example of how thermal energy density can beused to control operation of a part using in-situ measurements. FIG. 13shows how parts being produced using different combinations of energysource power and scan speed can be produced and then destructivelyanalyzed to determine a resulting part density in grams/cubiccentimeter, as depicted. In this trial, a part associated with partdensity 1302, which has a part density of 4.37 g/cc, was produced usingmanufacturer recommended scan and laser power settings. Based on theresulting density readings, a position of dashed line 1304 can bedetermined. Line 1304 indicates where a resulting lowered amount ofenergy input results in portions of the powder not being sufficientlyheated to fuse together resulting in part densities that fall below athreshold density level. Part density can also be reduced when too muchenergy is added to the system resulting in the formation of keyholeswithin the parts resulting from powder being vaporized instead of meltedduring a manufacturing operation. Dashed line 1306 can be experimentallydetermined from the density data and in this example is identified bypart density falling as low as 4.33 g/cc. Line 1308 represents anoptimum energy density contour along which energy density and partgeometry stay substantially constant. The density testing shows how theaverage density of parts created using settings distributed along line1308 stays relatively consistent.

FIGS. 14A-14B show a thermal energy density contour overlaid upon aportion of the power-density graph and illustrate how thermal densitymeasurements collected during an additive manufacturing operation varydepending on the settings used by the energy source. As depicted inFIGS. 14A-14B darker shading indicates higher thermal energy densitiesand lighter shading indicates lower thermal energy densities. From thepart density testing and thermal energy density contours, control limitscan be determined for a particular part. In this case, the controllimits, indicated by ellipse 1402, have been determined and allow powerand scan speed parameters to vary from the settings used to produce part1302 by up to 3σ along line 1308 and 1σ along a line perpendicular toline 1308. In some embodiments, the allowable variation in power andspeed allows for in-process adjustments to be made in order to maintaina desired thermal energy density during production runs. Ellipse 1404indicates an overall process window that can accommodate furthervariations outside the control limits. In some embodiments, this processwindow can be used to identify variations that would still allow aresulting part to be validated using only in-process data. It should benoted that while the depicted control and process windows are shown aselliptical, other process window shapes and sizes are possible and maybe more complex depending on, for example, part geometries and materialeccentricities. During a manufacturing operation, optical sensorreadings measuring thermal radiation can be used to determine thermalenergy density in situ. In instances where thermal energy density fallsoutside an expected range when keeping the laser power and scanvelocities within the depicted control limits indicated by ellipse 1402,portions of the part with the abnormal thermal energy density readingscan be flagged as potential having defects.

In some embodiments, the process windows can be incorporated into amodeling and simulation program that models one or more optical sensorsthat collect sensor readings used to determine thermal energy density.Once the modeling and simulation system iterates to a firstapproximation of an instruction set for a workpiece, expected thermalenergy density can be output to an additive manufacturing machine forfurther testing. The modeled thermal energy density data can used by theadditive manufacturing machine for further testing and validation, whenthe additive manufacturing machine includes an optical sensor andcomputing equipment configured to measure thermal energy density. Acomparison of the modeled and measured thermal energy density can beused to confirm how closely performance of the instructions set matchesthe expected outcome in situ.

FIG. 14C shows another power-density graph emphasizing various physicaleffects resulting from energy source settings falling too far out ofprocess window 1404. For example, the power density graph shows thatadding large amounts of laser power at low scanning velocities resultsin keyholes being formed within the workpiece. Keyhole formation occursdue to portions of the powder material evaporating due to receiving toomuch energy. Furthermore, low power combined with high scanningvelocities can result in a failure of the powder to fuse together.Finally, high power levels combined with high scanning velocities resultin fused metal balling up during a build operation. It should be notedthat varying a thickness of the deposited power layer can result in ashift of the lines separating the conduction mode zone from keyholeformation zones and lack of fusion zones. For example, increasing thethickness of the powder layers has the effect of increasing the slope ofthe lines separating the keyhole and lack of fusion regions from theconduction mode zone as a thicker layer generally requires more energyto undergo liquefaction.

FIG. 14C also shows how power and scan velocity settings correspondingto the conduction mode zone don't generally result in any of theaforementioned serious defects; however, by keeping the laser settingscorresponding to values within process window 1404 a resulting grainstructure and/or density of the part can be optimized. Another benefitof keeping the energy source settings within process window 1404 is thatthese settings should keep thermal energy density readings within anarrow range of values. Any thermal energy density values fallingoutside of the predetermined range during a manufacturing operation canbe indicative of a problem in the manufacturing process. These problemscan be addressed by moving the settings closer to a central region ofthe process window and/or by updating the process window for subsequentparts. In some embodiments, a manufacturer could discover that the faultwas caused by defective powder or some other infrequent aberration thatshould not be factored into subsequent manufacturing processes. Itshould be appreciated that the depicted process window may not be thesame for all portions of a part and could vary greatly depending on whatregion or even specific layer of the part was being worked on at aparticular time. The size and/or shape of process window 1404 can alsovary in accordance with other factors such as hatch spacing, scan lengthand scan direction in some embodiments.

FIG. 14D shows how a size and shape of a melt pool can vary inaccordance with laser power and scanning velocity settings. Exemplarymelt pools 1406-1418 exhibit exemplary melt pool size and shape forvarious laser power and velocity settings. In general, it can be seenthat larger melt pools result from higher amounts of laser power andlower scan velocities. However, for this particular configuration meltpool size depends more upon laser power than velocity.

FIGS. 15A-15F illustrate how a grid can be dynamically created tocharacterize and control an additive manufacturing operation. FIG. 15Ashows a top view of a cylindrical workpiece 1502 positioned upon aportion of a build plane 1504. Workpiece 1502 is shown as it undergoesan additive manufacturing operation. FIG. 15B shows how cylindricalworkpiece 1502 can be divided into multiple tracks 1506 along which anenergy source can melt powder distributed on an upper surface ofcylindrical workpiece 1502. In some embodiments, the energy source canalternate directions 1506 as depicted while in other embodiments theenergy source can always move in one direction. Furthermore a directionof tracks 1506 can vary from layer to layer in order to furtherrandomize the orientation of scans used to build workpiece 1502.

FIG. 15C shows an exemplary scan pattern for the energy source as itforms a portion of workpiece 1502. As indicated, by arrow 1508 adirection of movement of across workpiece 1502 of an exemplary energysource is diagonal. Individual scans 1510 of the energy source can beoriented in a direction perpendicular to the direction of movement ofthe energy source along track 1506 and extend entirely across track1506. The energy source can turn off briefly between successiveindividual scans 1510. In some embodiments, a duty cycle of the energysource can be about 90% as it traverses each of tracks 1506. Byemploying this type of scan strategy, the energy source can cover awidth of track 1506 as it traverses across workpiece 1502. In someembodiments, swath 1510 can have a width of about 5 mm. This cansubstantially reduce the number of tracks needed to form workpiece 1502as in some embodiments a width of a melt pool generated by the energysource can be on the order of about 80 microns.

FIGS. 15D-15E show how grid regions 1512 can be dynamically generatedalong each track 1506 and be sized to accommodate a width of eachindividual scan 1510. A precise position of subsequent scans can beforecast by the system by referencing energy source drive signalsenroute to the energy source. In some embodiments the width of grids1512 can match the length of individual scans 1512 or be within 10 or20% of the length of individual scans 1512. Again, scan length ofindividual scans 1512 can be anticipated by referencing the energysource drive signals. In some embodiments, grid regions 1512 can besquare or rectangular in shape. A thermal energy density can bedetermined for each of grid regions 1512 as the energy source continuesalong track 1506. In some embodiments, thermal energy density readingswithin grid region 1512-1 could be used to adjust an output of theenergy source within the next grid region, grid region 1512-2 in thiscase. For example, if the thermal energy density readings generated byindividual scans 1510 within grid region 1512-1 are substantially higherthan expected, energy source power output can be reduced, a speed atwhich energy source scans across individual scans 1510 can be increasedand/or spacing between individual scans 1510 can be increased withingrid region 1512-2. These adjustments can be made as part of a closedloop control system. While only five individual scans 1510 are shownwithin each region this is done for exemplary purposes only and theactual number of individual scans within a grid region 1512 can besubstantially higher. For example, where the melt zone generated by theenergy source is about 80 microns wide it could take about 60 individualscans 1510 in order for all the powder within a 5 mm square grid region1512 to fall within the melt zone.

FIG. 15F shows an edge region of workpiece 1502 once the energy sourcefinishes traversing the pattern of tracks 1506. In some embodiments, theenergy source can continue to add energy to workpiece 1502 subsequent toa majority of the powder having been melted and resolidified. Forexample, contour scans 1514 can track along an outer periphery 1516 ofworkpiece 1502 in order to apply a surface finish to workpiece 1502. Itshould be appreciated that contour scans 1514 as depicted aresubstantially shorter than individual scans 1510. For this reason, gridregions 1518 can be substantially narrower than grid regions 1512. Itshould also be noted that grid regions 1518 are not purely rectangularin shape as in this case they follow the contour of the outer peripheryof workpiece 1502. Another instances that may result in scan lengthdifferences could be where a workpiece includes walls of varyingthickness. A variable thickness wall could result in scans lengthvarying within a single grid region. In such a case, an area of eachgrid region could be kept consistent by increasing the length of thegrid region while narrowing the width to conform to changes in thelength of individual scans.

FIG. 16 shows a closed loop control example showing feedback controlloop 1600 for establishing and maintaining feedback control of anadditive manufacturing operation. At block 1602 a baseline thermalenergy density for the next grid region across which the energy sourceis about to traverse is input into the control loop. This baselinethermal energy density reading can be established from modeling andsimulation programs and/or from previously run experimental/test runs.In some embodiments, this baseline thermal energy density data can beadjusted by energy density bias block 1604 which includes energy densityreadings for various grid regions recorded during preceding layers.Energy density bias block 1604 can include an adjustment to baselineenergy density block in instances where preceding layers received toomuch or too little energy. For example, where optical sensor readingsindicate a thermal energy density below nominal in one region of aworkpiece, energy density bias values can increase the value of thebaseline energy density for grid regions overlapping the grid regionswith below nominal thermal energy density readings. In this way, theenergy source is able to fuse additional powder that was not fully fusedduring the preceding layer or layers.

FIG. 16 also shows how the inputs from block 1602 and 1604 cooperativelycreate an energy density control signal that is received by controller1606. Controller 1606 is configured to receive the energy densitycontrol signal and generate heat source input parameters configured togenerate the desired thermal energy density within the current gridregion. Input parameters can include power, scan velocity, hatchspacing, scan direction and scan duration. The input parameters are thenreceived by energy source 1608 and any changes in the input parametersare adopted by energy source 1608 for the current grid region. Onceoptical sensors measure the scans of energy source 1608 making up thecurrent grid region, at block 1610 thermal energy density for thecurrent grid region is calculated and compared to the energy densitycontrol signal. If the two values are the same then no change to energydensity control signal is made based upon the optical sensor data.However, if the two values are different the difference is added orsubtracted from the energy density control signal for scans made in thenext grid region.

In some embodiments, grid regions for the current layer and allpreceding layers can be dynamically generated grid regions that areoriented in accordance with a path and scan length/width of scansperformed by the energy source. In this type of configuration bothbaseline energy density and energy density bias can both be based ondynamically generated grid regions. In other embodiments, grid regionsfor the current layer can be dynamically generated while energy densitybias data 1604 can be based upon energy density readings associated withstatic grid regions defined prior to the beginning of the additivemanufacturing operation, resulting in the static grid regions remainingfixed throughout the part and not varying in position, size or shape.The grids could be uniformly shaped and spaced when a Cartesian gridsystem is desired but could also take the form of grid regions making upa polar grid system. In other embodiments, the grid regions for thecurrent layer can be statically generated prior to the build operationbeing carried out and energy density bias data can also be staticallygenerated and share the same grid being used for the current layer.

In some embodiments, thermal emission density can be used in lieu ofthermal energy density with control loop 1600. Thermal emission densitycan refer to other factors in addition to thermal energy density. Forexample, thermal emission density can be a weighted average of multiplefeatures that include thermal energy density along with one or moreother features such as peak temperature, minimum temperature, heatingrate, cooling rate, average temperature, standard deviation from averagetemperature, and a rate of change of the average temperature over time.In other embodiments, one or more of the other features could be used tovalidate that the scans making up each of the grid regions are reachinga desired temperature, heating rate or cooling rate. In such anembodiment the validation features could be used as a flag to indicatethat input parameters for the energy source may need to be adjustedwithin a defined control window to achieve the desired temperature,heating rate or cooling rate. For example, if peak temperature withinthe grid region is too low power could be increased and/or scanningvelocity decreased. Although discussion of aforementioned control loop1600 related to various types of grid TED it should be noted that aperson with ordinary skill in the art would also understand that scanTED metrics could also be used in a similar loop configuration.

TED Analysis for Recoater Arm Short Feed

FIG. 17A shows a normal distribution of powder 1702 across a build plate1704. This depiction shows how the powder gets spread evenly without anyvariations in height. In contrast, FIG. 17B shows how when aninsufficient amount of powder 1702 is retrieved by a recoater arm, athickness of powder layer 1702 can vary greatly. Once the recoater armbegins to run out of powder 1702 a thickness of the layer of powder 1702gradually tapers off until portions of build plate 1704 are leftentirely bare of powder 1702. As errors of this sort can have quitenegative effects upon the overall quality of the part early detection ofthis type of phenomenon is important for accurate defect detection.

FIG. 17C shows a black and white photo of a build plate in which a shortfeed of powder 1702 resulted in only partial coverage or nine workpiecesarranged on a build plane. In particular, three of the workpieces on theright side of the photo are completely covered while three of theworkpieces on the left side are almost completely uncovered.

FIG. 17D shows how when an energy source scans across all nineworkpieces using the same input parameters, detected thermal energydensity is substantially different. Regions 1706 produce substantiallyhigher thermal energy density readings on account of the emissivity ofthe powder being substantially higher and the thermal conductivity ofthe build plate or solidified powder material being greater than theeffective thermal conductivity of the powder. The higher thermalconductivity reduces the amount of energy available for radiation backtoward optical sensors thereby reducing detected thermal radiation.Furthermore, the lower emissivity of the material itself also reducesthe amount of radiated thermal energy. F

Thermal Energy Density vs Global Energy Density

Power provided by an energy source coming into the workpiece results inmelting of material making the workpiece, but that power can also bedissipated by several other heat and mass transfer processes during anadditive manufacturing process. The following equation describesdifferent processes that can absorb the power emitted where the energysource is a laser scanning across a powder bed:P TOTAL LASER POWER =P OPTICAL LOSSES AT THE LASER +P ABSORPTION BYCHAMBER GAS +P REFLECTION +P PARTICLE AND PLUME INTERACTIONS +P POWERNEEDED TO SUSTAIN MELT POOL +P CONDUCTION LOSSES +P RADIATION LOSSES +PCONVECTION LOSSES +P VAPORIZATION LOSSES  Eq(13)

Optical losses at the laser refers to power losses due to imperfectionsin the optical system responsible for transmitting and focusing laserlight on the build plane. The imperfections result in absorption andreflection losses of the emitted laser within the optical system.Absorption by chamber gas refers to power loss due to gases within abuild chamber of the additive manufacturing system absorbing a smallfraction of the laser power. The impact of this power loss will dependon the absorptivity of the gas at the wavelength of the laser.Reflection losses refer to power lost due to light escaping the laseroptics that is never absorbed by the powder bed. Particle and plumeinteractions refer to interactions between the laser and a plume and/orparticles ejected during the deposition process. While power loss due tothese affects can be ameliorated though shielding gas being circulatedthrough the build chamber a small amount of power reduction generallycan't be completely avoided. Power needed to sustain melt pool refers tothe portion of the laser power absorbed by the working material formelting and ultimately superheating the powder to whatever temperaturethe melt pool ultimately achieves. Conduction losses refers to theportion of the power absorbed through conduction to solidified metalbelow the powder and the powder bed itself. In this way, the powder bedand solidified material making up the part will conduct heat away fromthe melt pool. This conductive transfer of thermal energy is thedominant form of energy loss from the melt pool. Radiation losses refersto that portion of the laser power that is emitted by the melt pool andmaterial surrounding the melt pool that is hot enough to emit thermalradiation. Convection losses refer to losses caused by the transfer ofheat energy to gases circulating through the build chamber. Finally,vaporization losses refer to a small fraction of powder material thatwill vaporize under laser irradiation. The latent heat of vaporizationis very large, so this is a powerful cooling effect on the melt pool andcan be a non-negligible source of energy loss as the total laser beampower goes higher.

The thermal energy density (TED) metric is based on measurement ofoptical light that is a result of the radiation of light from the heatedregions, transmission of this light back through the optics, collectionof the light by the detector, and conversion of this light intoelectrical signals. The equation governing blackbody radiation over allpossible frequencies is given by the Stefan-Boltzmann equation shownbelow in Eq (14):P _(RADIATED) =F _(HOT-OPTICS) ·ε·σ·A·(T _(HOT) ⁴ −T _(BACKGROUND)⁴)  Eq(14)

The variables from Eq (14) are shown below in Table (1).

TABLE 1 E Emissivity is defined as the ratio of the energy radiated froma material's surface to that radiated from a blackbody (a perfectemitter) at the same temperature and wavelength and under the sameviewing conditions. F_(HOT-OPTICS) This is the view factor between theregions of the build plane hot enough to radiate and the optics of thelaser scan head. Σ The Stefan-Boltzmann constant, 5.67 × 10 −8 watt permeter squared per degree kelvin to the fourth power A The area of theregions hot enough to radiate energy at any substantial amount. Notethat in general this will be LARGER than the area of the melt pool. Inother words, this term is NOT equivalent to the melt pool area. T_(HOT)The average temperature in degrees K of the region hot enough to radiatein any significant amount. T_(BACKGROUND) The temperature of the objector background medium to which the melt pool is radiating. This willgenerally be a significantly lower temperature, and therefore inpractice this term is of negligible magnitude

There are additional intervening factors impacting the radiated lightbefore it is collected by the sensor and before it results in a voltagethat is used to calculate the TED metric. This is summarized below in Eq(15):V VOLTAGE USED BY TED ={P RADIATED −P VIEW FACTOR −P OPTICAL LOSSES ATRADIATED WAVELENGTHS −P SENSOR LOSS FACTOR}*(SENSOR SCALINGFACTOR)  Eq(15)

These various terms from Eq (15) are explained in Table (2) below.

TABLE (2) P_(RADIATED) The radiated power previously discussed as thepower radiated from the regions of the build plane (comprised of themelt pool and surrounding hot material) P_(VIEW FACTOR) This loss termtakes into account that not all of the radiated power leaving the hotregion will enter the optics of the scan head, and that there is ageometric view factor effect governing the amount of energy that can becollected. This could be numerically calculated by knowing all therelevant geometries and employing a ray tracing scheme.P_(OPTICAL LOSSES AT RADIATED) This term accounts for losses due to theoptics of the scan head and _(WAVELENGTHS) associated partiallyreflective and wavelength dependent mirrors that allow the light to goback through the scan head optics and to be collected at the sensorP_(SENSOR LOSS FACTOR) The sensor itself will have wavelength-dependentabsorption characteristics SENSOR SCALING This is a numerical factor forhow photons received by photodiode FACTOR are converted to electrons andresult in a measureable voltage

Often in additive manufacturing, a figure of merit that is used is theglobal energy density (GED). This is a parameter that combines variousPROCESS INPUTS as shown below in Eq (16):GED=(BEAM POWER)/{(TRAVEL SPEED)*(HATCH SPACING)}  Eq(16)

We notice that GED has units of energy per unit area:(JOULES/SEC)/{(CM/SEC)*(CM)}=JOULES/CM². However, it should be notedthat although GED may have the same unites as TED, GED and TED are NOTgenerally equivalent. As an example, TED is derived from the radiatedpower from the hot region divided by an area, whereas GED is a measureof input power. As described herein, TED relates to RESPONSE or PROCESSOUTPUT, whereas GED relates to a PROCESS INPUT. The inventors believethat, as a result, TED and GED are different measures from each other.In some embodiments, the area utilized in determining TED differs fromthe melt pool area. As a result, some embodiments do not have a directcorrelation between TED and the melt pool area. Beneficially, TED issensitive to a wide range of factors which directly impact the additivemanufacturing process.

While the embodiments described herein have used data generated byoptical sensors to determine the thermal energy density, the embodimentsdescribed herein may be implemented using data generated by sensors thatmeasure other manifestations of in-process physical variables. Sensorsthat measure manifestations of in-process physical variables include,for example, force and vibration sensors, contact thermal sensors,non-contact thermal sensors, ultrasonic sensors, and eddy currentsensors. It will be apparent to one of ordinary skill in the art thatmany modifications and variations are possible in view of the aboveteachings.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination.Various aspects of the described embodiments can be implemented bysoftware, hardware or a combination of hardware and software. Thedescribed embodiments can also be embodied as computer readable code ona computer readable medium for controlling manufacturing operations oras computer readable code on a computer readable medium for controllinga manufacturing line. The computer readable medium is any data storagedevice that can store data which can thereafter be read by a computersystem. Examples of the computer readable medium include read-onlymemory, random-access memory, CD-ROMs, HDDs, DVDs, magnetic tape, andoptical data storage devices. The computer readable medium can also bedistributed over network-coupled computer systems so that the computerreadable code is stored and executed in a distributed fashion.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It will be apparent to one of ordinary skill inthe art that many modifications and variations are possible in view ofthe above teachings.

What is claimed is:
 1. An additive manufacturing method formanufacturing a component, the method comprising: depositing a layer ofpowdered metal on a build plane; dividing at least a portion of eachbuild plane into a plurality of grid regions, each grid region having arespective grid area; generating a plurality of scans across eachrespective grid region using an energy source to fuse the layer ofpowdered metal; determining an amount of energy radiated from each gridregion during the respective plurality of scans for that grid region;determining a thermal energy density for each grid region; and comparingthe thermal energy density for each grid region to a baseline value toidentify potentially defective grid regions.
 2. The method of claim 1further comprising displaying potentially defective grid regions using agraphical medium.
 3. The method of claim 1 further comprisingrepresenting potentially defective grid regions using a text-basedmedium.
 4. The method of claim 1 wherein the energy source is a laserand in response to determining that a grid region is potentiallydefective, adjusting subsequent scans of the laser to repair that gridregion.
 5. The method of claim 1 wherein the identification ofpotentially defective grid regions includes classifying each grid regionof the plurality of grid regions as nominal or off-nominal.
 6. Themethod of claim 5 wherein each off-nominal grid region is classifiedbased on a set of off-nominal thresholds.
 7. An additive manufacturingmethod comprising: dividing a build plane into a plurality of gridregions, wherein each of the grid regions has a respective grid area;generating a plurality of scans of an energy source across the buildplane; determining a total amount of energy radiated from each gridregion during the plurality of scans; computing a thermal energy densityassociated with each grid region of the plurality of grid regions basedupon the total amount of energy radiated from each respective gridregion and a grid area of each respective grid region; and comparing thecomputed thermal energy density for each grid region of the plurality ofgrid regions to a baseline energy density value to identify potentiallydefective grid regions.
 8. The method of claim 7 further comprisingdisplaying potentially defective grid regions using a graphical medium.9. The method of claim 7 further comprising representing potentiallydefective grid regions using a text-based medium.
 10. The method ofclaim 7 wherein the energy source is a laser and in response todetermining that a grid region is potentially defective, adjustingsubsequent scans of the laser to repair that grid region.
 11. The methodof claim 7 wherein the identification of potentially defective gridregions includes classifying each grid region of the plurality of gridregions as nominal or off-nominal.
 12. The method of claim 11 whereineach off-nominal grid region is further classified based on a set ofoff-nominal thresholds.
 13. The method of claim 7 wherein the thermalenergy density for each grid region is determined by dividing the totalamount of energy radiated from each respective grid region by a gridarea of that grid region.
 14. An additive manufacturing method formanufacturing a component, the method comprising: depositing a layer ofpowdered metal on a build plane; fusing the layer of powdered metalusing an energy source to generate a plurality of scans across the buildplane; measuring an amount of energy radiated from the build planeduring the plurality of scans; determining a thermal energy density forthe build plane based upon an amount of energy radiated and an area ofthe build plane traversed by the plurality of scans; and determining ifthe build plane is potentially defective by comparing the thermal energydensity to a baseline thermal energy density value.
 15. The method ofclaim 14 further comprising dividing the build plane into a plurality ofgrid regions, with each grid region having a respective grid area. 16.The method of claim 15 further comprising determining a thermal energydensity for each grid region and comparing the determined thermal energydensity to a baseline thermal energy density to determine if that gridregion is potentially defective.
 17. The method of claim 14 furthercomprising displaying potentially defective build planes using agraphical medium.
 18. The method of claim 14 further comprisingrepresenting potentially defective build planes using a text-basedmedium.
 19. The method of claim 14 wherein the energy source is a laserand in response to determining that a build plane is potentiallydefective, adjusting subsequent scans of the laser to repair the buildplane.
 20. The method of claim 14 wherein the thermal energy density forthe build plane is determined by dividing the amount of energy radiatedfrom the build plane by the area of the build plane traversed by theplurality of scans.